This paper investigates the effects of spatial filtering on the ensemble based estimate of the background error covariance matrix in an ensemble based Kalman filter (EnKF). In particular, a novel kernel smoothing method with variable bandwidth is introduced and its performance is compared to that of the widely used Gaspari-Cohn filter, which uses a fifth order kernel function with a fixed local...

In this paper, we propose a new fast Fourier transform (FFT) based LMS-Newton (LMSN) adaptive filter algorithm. At each adaptive time step t, the nth-order filter coefficients are updated by using the inverse of an n-by-n Hermitian, positive definite, Toeplitz operator T (t). By applying the cyclic displacement formula for the inverse of a Toeplitz operator, T (t)−1 can be constructed using the...

—Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate seriously. To improve the robustness of KF against impulsive noises, we propose in ...

A method for object tracking combining the accuracy of mean shift with the robustness to occlusion of Kalman filtering is proposed. At first, an estimation of the object’s position is obtained by the mean shift tracking algorithm and it is treated as the observation for a Kalman filter. Moreover, we propose a dynamic scheme for the Kalman filter as the elements of its state matrix are updated o...

In this paper, the important problem of single-channel noise reduction is treated from a new perspective. The problem is posed as a filtering problem based on joint diagonalization of the covariance matrices of the desired and noise signals. More specifically, the eigenvectors from the joint diagonalization corresponding to the least significant eigenvalues are used to form a filter, which effe...

In this paper, we study asymptotic stability properties of risk-sensitive filters with respect to their initial conditions. In particular, we consider a linear time-invariant systems with initial conditions that are not necessarily Gaussian. We show that in the case of Gaussian initial conditions, the optimal risksensitive filter asymptotically converges to any suboptimal filter initialized wit...

We study the design of signal–adapted FIR paraunitary filter banks, using energy compaction as the adaptation criterion. We present some important properties that globally optimal solutions to this optimization problem satisfy. In particular, we show that the optimal filters in the first channel of the filter bank are spectral factors of the solution to a linear semi–infinite programming (SIP) ...

In this paper, we develop a novel and effective algorithm for the construction of perfect reconstruction filter banks (PRFBs) with linear phase. In the algorithm, the key step is the symmetric Laurent polynomial matrix extension (SLPME). There are two typical problems in the construction: (1) For a given symmetric finite low-pass filter a with the polyphase, to construct a PRFBs with linear pha...

We study Chebyshev filter diagonalization as a tool for the computation of many interior eigenvalues of very large sparse symmetric matrices. In this technique the subspace projection onto the target space of wanted eigenvectors is approximated with high-order filter polynomials obtained from a regularized Chebyshev expansion of a window function. After a short discussion of the conceptual foun...

ion abstraction parallelisation translation translation translation system transformation user transformation third party transformation A L application in language L HL harness in language L Figure 1. intermediate ML development ML software to C++ (this stage has not yet been automated, though previous work shows it is possible [5]), and this is done at the end of the development cycle. Transl...